Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei


Abstract
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.
Anthology ID:
S18-1046
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
313–318
Language:
URL:
https://aclanthology.org/S18-1046
DOI:
10.18653/v1/S18-1046
Bibkey:
Cite (ACL):
Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, and Ran Wei. 2018. Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 313–318, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets (Chi et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1046.pdf